Journal Description
Technologies
Technologies
is an international, peer-reviewed, open access journal singularly focusing on emerging scientific and technological trends and is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within ESCI (Web of Science), Scopus, Inspec, INSPIRE, and other databases.
- Journal Rank: CiteScore - Q1 (Computer Science (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.7 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.6 (2022);
5-Year Impact Factor:
3.1 (2022)
Latest Articles
Inference Analysis of Video Quality of Experience in Relation with Face Emotion, Video Advertisement, and ITU-T P.1203
Technologies 2024, 12(5), 62; https://doi.org/10.3390/technologies12050062 - 03 May 2024
Abstract
This study introduces an FER-based machine learning framework for real-time QoE assessment in video streaming. This study’s aim is to address the challenges posed by end-to-end encryption and video advertisement while enhancing user QoE. Our proposed framework significantly outperforms the base reference, ITU-T
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This study introduces an FER-based machine learning framework for real-time QoE assessment in video streaming. This study’s aim is to address the challenges posed by end-to-end encryption and video advertisement while enhancing user QoE. Our proposed framework significantly outperforms the base reference, ITU-T P.1203, by up to 37.1% in terms of accuracy and 21.74% after attribute selection. Our study contributes to the field in two ways. First, we offer a promising solution to enhance user satisfaction in video streaming services via real-time user emotion and user feedback integration, providing a more holistic understanding of user experience. Second, high-quality data collection and insights are offered by collecting real data from diverse regions to minimize any potential biases and provide advertisement placement suggestions.
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(This article belongs to the Section Information and Communication Technologies)
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New Upgrade to Improve Operation of Conventional Grid-Connected Photovoltaic Systems
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Manuel Cáceres, Alexis Raúl González Mayans, Andrés Firman, Luis Vera and Juan de la Casa Higueras
Technologies 2024, 12(5), 61; https://doi.org/10.3390/technologies12050061 - 02 May 2024
Abstract
The incorporation of distributed generation with photovoltaic systems entails a drawback associated with intermittency in the generation capacity due to variations in the solar resource. In general, this aspect limits the level of penetration that this resource can have without producing an appreciable
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The incorporation of distributed generation with photovoltaic systems entails a drawback associated with intermittency in the generation capacity due to variations in the solar resource. In general, this aspect limits the level of penetration that this resource can have without producing an appreciable impact on the quality of the electrical supply. With the intention of reducing its intermittency, this paper presents the characterization of a methodology for maximizing grid-connected PV system operation under low-solar-radiation conditions. A new concept of a hybrid system based on a constant current source and capable of integrating different sources into a conventional grid-connected PV system is presented. Results of an experimental characterization of a low-voltage grid–PV system connection with a DC/DC converter for constant-current source application are shown in zero and non-zero radiation conditions. The results obtained demonstrate that the proposed integration method works efficiently without causing appreciable effects on the parameters that define the quality of the electrical supply. In this way, it is possible to efficiently incorporate another source of energy, taking advantage of the characteristics of the GCPVS without further interventions in the system. It is expected that this topology could help to integrate other generation and/or storage technologies into already existing PV systems, opening a wide field of research in the PV systems area.
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(This article belongs to the Collection Electrical Technologies)
Open AccessArticle
A Cyber–Physical System Based on Digital Twin and 3D SCADA for Real-Time Monitoring of Olive Oil Mills
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Cristina Martinez-Ruedas, Jose-Maria Flores-Arias, Isabel M. Moreno-Garcia, Matias Linan-Reyes and Francisco Jose Bellido-Outeiriño
Technologies 2024, 12(5), 60; https://doi.org/10.3390/technologies12050060 - 30 Apr 2024
Abstract
Cyber–physical systems involve the creation, continuous updating, and monitoring of virtual replicas that closely mirror their physical counterparts. These virtual representations are fed by real-time data from sensors, Internet of Things (IoT) devices, and other sources, enabling a dynamic and accurate reflection of
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Cyber–physical systems involve the creation, continuous updating, and monitoring of virtual replicas that closely mirror their physical counterparts. These virtual representations are fed by real-time data from sensors, Internet of Things (IoT) devices, and other sources, enabling a dynamic and accurate reflection of the state of the physical system. This emphasizes the importance of data synchronization, visualization, and interaction within virtual environments as a means to improve decision-making, training, maintenance, and overall operational efficiency. This paper presents a novel approach to a cyber–physical system that integrates virtual reality (VR)-based digital twins and 3D SCADA in the context of Industry 4.0 for the monitoring and optimization of an olive mill. The methodology leverages virtual reality to create a digital twin that enables immersive data-driven simulations for olive mill monitoring. The proposed CPS takes data from the physical environment through the existing sensors and measurement elements in the olive mill, concentrates them, and exposes them to the virtual environment through the Open Platform Communication United Architecture (OPC-UA) protocol, thus establishing bidirectional and real-time communication. Furthermore, in the proposed virtual environment, the digital twin is interfaced with the 3D SCADA system, allowing it to create virtual models of the process. This innovative approach has the potential to revolutionize the olive oil industry by improving operational efficiency, product quality, and sustainability while optimizing maintenance practices.
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(This article belongs to the Topic Cyber-Physical Security for IoT Systems)
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Neural Network-Based Body Weight Prediction in Pelibuey Sheep through Biometric Measurements
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Alfonso J. Chay-Canul, Enrique Camacho-Pérez, Fernando Casanova-Lugo, Omar Rodríguez-Abreo, Mayra Cruz-Fernández and Juvenal Rodríguez-Reséndiz
Technologies 2024, 12(5), 59; https://doi.org/10.3390/technologies12050059 - 30 Apr 2024
Abstract
This paper presents an intelligent system for the dynamic estimation of sheep body weight (BW). The methodology used to estimate body weight is based on measuring seven biometric parameters: height at withers, rump height, body length, body diagonal length, total body length, semicircumference
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This paper presents an intelligent system for the dynamic estimation of sheep body weight (BW). The methodology used to estimate body weight is based on measuring seven biometric parameters: height at withers, rump height, body length, body diagonal length, total body length, semicircumference of the abdomen, and semicircumference of the girth. A biometric parameter acquisition system was developed using a Kinect as a sensor. The results were contrasted with measurements obtained manually with a flexometer. The comparison gives an average root mean square error (RMSE) of 9.91 and a mean of 0.81. Subsequently, the parameters were used as input in a back-propagation artificial neural network. Performance tests were performed with different combinations to make the best choice of architecture. In this way, an intelligent body weight estimation system was obtained from biometric parameters, with a 5.8% RMSE in the weight estimations for the best architecture. This approach represents an innovative, feasible, and economical alternative to contribute to decision-making in livestock production systems.
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(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering II)
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RFID Tags for On-Metal Applications: A Brief Survey
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Emanuel Pereira, Sandoval Júnior, Luís Felipe Vieira Silva, Mateus Batista, Eliel Santos, Ícaro Araújo, Jobson Araújo, Erick Barboza, Francisco Gomes, Ismael Trindade Fraga, Daniel Oliveira Dos Santos and Roger Davanso
Technologies 2024, 12(5), 58; https://doi.org/10.3390/technologies12050058 - 27 Apr 2024
Abstract
Radio-frequency identification technology finds extensive use in various industrial applications, including those involving metallic surfaces. The integration of radio-frequency identification systems with metal surfaces, such as those found in the automotive sector, presents distinct challenges that can notably affect system efficacy due to
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Radio-frequency identification technology finds extensive use in various industrial applications, including those involving metallic surfaces. The integration of radio-frequency identification systems with metal surfaces, such as those found in the automotive sector, presents distinct challenges that can notably affect system efficacy due to metal’s tendency to reflect electromagnetic waves, thus degrading the functionality of conventional radio-frequency identification tags. This highlights the importance of conducting research into academic publications and patents to grasp the current advancements and challenges in this field, aiming to improve the applications of radio-frequency identification tags technology on metal. Consequently, this research undertakes a concise review of both the literature and patents exploring radio-frequency identification technology’s use for on-metal tags, utilizing resources like Google Scholar and Google Patents. The research categorized crucial aspects such as tag flexibility, operating frequency, and geographic origins of the research. Findings highlight China’s prominent role in contributing to metal-focused radio-frequency identification tag research, with a considerable volume of articles and patents. In particular, flexible tags and the Ultra-High Frequency range are dominant in both scholarly and patent documents, reflecting their significance in radio-frequency identification technology applications. The research underscores a vibrant area of development within radio-frequency identification technology, with continued innovation driven by specific industrial needs. Despite the noted advances, the presence of a significant percentage of no longer valid patents suggests substantial opportunities for further research and innovation in radio-frequency identification technology for on-metal applications, especially considering the demand for flexible tags and for solutions in systems that offer specialized characteristics or are tailored for specific uses.
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(This article belongs to the Section Information and Communication Technologies)
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Open AccessArticle
Miniaturized Microstrip Dual-Channel Diplexer Based on Modified Meander Line Resonators for Wireless and Computer Communication Technologies
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Yaqeen Sabah Mezaal, Shahad K. Khaleel, Ban M. Alameri, Kadhum Al-Majdi and Aqeel A. Al-Hilali
Technologies 2024, 12(5), 57; https://doi.org/10.3390/technologies12050057 - 24 Apr 2024
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There has been a lot of interest in microstrip diplexers lately due to their potential use in numerous wireless and computer communication technologies, including radio broadcasts, mobile phones, broadband wireless, and satellite-based communication systems. It can do this because it has a communication
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There has been a lot of interest in microstrip diplexers lately due to their potential use in numerous wireless and computer communication technologies, including radio broadcasts, mobile phones, broadband wireless, and satellite-based communication systems. It can do this because it has a communication channel that can combine two distinct filters into one. This article presents a narrow-band microstrip diplexer that uses a stepped impedance resonator, a uniform impedance resonator, tiny square patches, and a meander line resonator. The projected diplexer might be made smaller than its initial dimensions by utilizing the winding construction. To model the microstrip diplexer topology for WiMAX and WIFI/WLAN at 1.66 GHz and 2.52 GHz, the Advanced Wave Research (AWR) solver was employed. It exhibited an insertion loss of 3.2 dB and a return loss of 16 dB for the first channel, while the insertion loss and return loss were 2.88 dB and 21 dB, respectively, for the second channel. When both filters were simulated, the band isolation was 31 dB. The projected microstrip diplexer has been fabricated using an FR4 epoxy laminate with dimensions of 32 × 26 mm2. The simulated S-parameters phase and group delay closely matched the measurements.
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Open AccessArticle
An End-to-End Lightweight Multi-Scale CNN for the Classification of Lung and Colon Cancer with XAI Integration
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Mohammad Asif Hasan, Fariha Haque, Saifur Rahman Sabuj, Hasan Sarker, Md. Omaer Faruq Goni, Fahmida Rahman and Md Mamunur Rashid
Technologies 2024, 12(4), 56; https://doi.org/10.3390/technologies12040056 - 21 Apr 2024
Abstract
To effectively treat lung and colon cancer and save lives, early and accurate identification is essential. Conventional diagnosis takes a long time and requires the manual expertise of radiologists. The rising number of new cancer cases makes it challenging to process massive volumes
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To effectively treat lung and colon cancer and save lives, early and accurate identification is essential. Conventional diagnosis takes a long time and requires the manual expertise of radiologists. The rising number of new cancer cases makes it challenging to process massive volumes of data quickly. Different machine learning approaches to the classification and detection of lung and colon cancer have been proposed by multiple research studies. However, when it comes to self-learning classification and detection tasks, deep learning (DL) excels. This paper suggests a novel DL convolutional neural network (CNN) model for detecting lung and colon cancer. The proposed model is lightweight and multi-scale since it uses only 1.1 million parameters, making it appropriate for real-time applications as it provides an end-to-end solution. By incorporating features extracted at multiple scales, the model can effectively capture both local and global patterns within the input data. The explainability tools such as gradient-weighted class activation mapping and Shapley additive explanation can identify potential problems by highlighting the specific input data areas that have an impact on the model’s choice. The experimental findings demonstrate that for lung and colon cancer detection, the proposed model was outperformed by the competition and accuracy rates of 99.20% have been achieved for multi-class (containing five classes) predictions.
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(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence)
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Digital Twin Models for Personalised and Predictive Medicine in Ophthalmology
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Miruna-Elena Iliuţă, Mihnea-Alexandru Moisescu, Simona-Iuliana Caramihai, Alexandra Cernian, Eugen Pop, Daniel-Ioan Chiş and Traian-Costin Mitulescu
Technologies 2024, 12(4), 55; https://doi.org/10.3390/technologies12040055 - 18 Apr 2024
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This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured
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This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured architectural framework comprising five levels has been proposed, integrating Digital Twin, Systems Medicine, and Predictive Medicine for managing eye diseases. Based on demographic parameters, statistics were performed to identify potential correlations that may contribute to predispositions to glaucoma. With the aid of a dataset, a neural network was trained with the goal of identifying glaucoma. This comprehensive approach, based on statistical analysis and Machine Learning, is a promising method to enhance diagnostic accuracy and provide personalized treatment approaches.
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Open AccessArticle
Experimental and Numerical Analysis of a Novel Cycloid-Type Rotor versus S-Type Rotor for Vertical-Axis Wind Turbine
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José Eli Eduardo González-Durán, Juan Manuel Olivares-Ramírez, María Angélica Luján-Vega, Juan Emigdio Soto-Osornio, Juan Manuel García-Guendulain and Juvenal Rodriguez-Resendiz
Technologies 2024, 12(4), 54; https://doi.org/10.3390/technologies12040054 - 17 Apr 2024
Abstract
The performance of a new vertical-axis wind turbine rotor based on the mathematical equation of the cycloid is analyzed and compared through simulation and experimental testing against a semicircular or S-type rotor, which is widely used. The study examines three cases: equalizing the
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The performance of a new vertical-axis wind turbine rotor based on the mathematical equation of the cycloid is analyzed and compared through simulation and experimental testing against a semicircular or S-type rotor, which is widely used. The study examines three cases: equalizing the diameter, chord length and the area under the curve. Computational Fluid Dynamics (CFD) was used to simulate these cases and evaluate moment, angular velocity and power. Experimental validation was carried out in a wind tunnel that was designed and optimized with the support of CFD. The rotors for all three cases were 3D printed in resin to analyze their experimental performance as a function of wind speed. The moment and Maximum Power Point (MPP) were determined in each case. The simulation results indicate that the cycloid-type rotor outperforms the semicircular or S-type rotor by 15%. Additionally, experimental evidence confirms that the cycloid-type rotor performs better in all three cases. In the MPP analysis, the cycloid-type rotor achieved an efficiency of 10.8% which was 38% better than the S-type rotor.
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(This article belongs to the Section Environmental Technology)
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Developing a Performance Evaluation Framework Structural Model for Educational Metaverse
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Elena Tsappi, Ioannis Deliyannis and George Nathaniel Papageorgiou
Technologies 2024, 12(4), 53; https://doi.org/10.3390/technologies12040053 - 16 Apr 2024
Abstract
In response to the transformative impact of digital technology on education, this study introduces a novel performance management framework for virtual learning environments suitable for the metaverse era. Based on the Structural Equation Modeling (SEM) approach, this paper proposes a comprehensive evaluative model,
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In response to the transformative impact of digital technology on education, this study introduces a novel performance management framework for virtual learning environments suitable for the metaverse era. Based on the Structural Equation Modeling (SEM) approach, this paper proposes a comprehensive evaluative model, anchored on the integration of the Theory of Planned Behavior (TPB), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Community of Inquiry Framework (CoI). The model synthesizes five Key Performance Indicators (KPIs)—content delivery, student engagement, metaverse tool utilization, student performance, and adaptability—to intricately assess academic avatar performances in virtual educational settings. This theoretical approach marks a significant stride in understanding and enhancing avatar efficacy in the metaverse environment. It enriches the discourse on performance management in digital education and sets a foundation for future empirical studies. As virtual online environments gain prominence in education and training, this research study establishes the basic principles and highlights the key points for further empirical research in the new era of the metaverse educational environment.
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(This article belongs to the Topic Theoretical and Applied Problems in Human-Computer Intelligent Systems)
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A Comparison of Machine Learning-Based and Conventional Technologies for Video Compression
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Lesia Mochurad
Technologies 2024, 12(4), 52; https://doi.org/10.3390/technologies12040052 - 15 Apr 2024
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The growing demand for high-quality video transmission over bandwidth-constrained networks and the increasing availability of video content have led to the need for efficient storage and distribution of large video files. To improve the latter, this article offers a comparison of six video
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The growing demand for high-quality video transmission over bandwidth-constrained networks and the increasing availability of video content have led to the need for efficient storage and distribution of large video files. To improve the latter, this article offers a comparison of six video compression methods without loss of quality. Particularly, H.255, VP9, AV1, convolutional neural network (CNN), recurrent neural network (RNN), and deep autoencoder (DAE). The proposed decision is to use a dataset of high-quality videos to implement and compare the performance of classical compression algorithms and algorithms based on machine learning. Evaluations of the compression efficiency and the quality of the received images were made on the basis of two metrics: PSNR and SSIM. This comparison revealed the strengths and weaknesses of each approach and provided insights into how machine learning algorithms can be optimized in future research. In general, it contributed to the development of more efficient and effective video compression algorithms that can be useful for a wide range of applications.
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Open AccessArticle
Monitoring of Hip Joint Forces and Physical Activity after Total Hip Replacement by an Integrated Piezoelectric Element
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Franziska Geiger, Henning Bathel, Sascha Spors, Rainer Bader and Daniel Kluess
Technologies 2024, 12(4), 51; https://doi.org/10.3390/technologies12040051 - 09 Apr 2024
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Resultant hip joint forces can currently only be recorded in situ in a laboratory setting using instrumented total hip replacements (THRs) equipped with strain gauges. However, permanent recording is important for monitoring the structural condition of the implant, for therapeutic purposes, for self-reflection,
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Resultant hip joint forces can currently only be recorded in situ in a laboratory setting using instrumented total hip replacements (THRs) equipped with strain gauges. However, permanent recording is important for monitoring the structural condition of the implant, for therapeutic purposes, for self-reflection, and for research into managing the predicted increasing number of THRs worldwide. Therefore, this study aims to investigate whether a recently proposed THR with an integrated piezoelectric element represents a new possibility for the permanent recording of hip joint forces and the physical activities of the patient. Hip joint forces from nine different daily activities were obtained from the OrthoLoad database and applied to a total hip stem equipped with a piezoelectric element using a uniaxial testing machine. The forces acting on the piezoelectric element were calculated from the generated voltages. The correlation between the calculated forces on the piezoelectric element and the applied forces was investigated, and the regression equations were determined. In addition, the voltage outputs were used to predict the activity with a random forest classifier. The coefficient of determination between the applied maximum forces on the implant and the calculated maximum forces on the piezoelectric element was R2 = 0.97 (p < 0.01). The maximum forces on the THR could be determined via activity-independent determinations with a deviation of 2.49 ± 13.16% and activity-dependent calculation with 0.87 ± 7.28% deviation. The activities could be correctly predicted using the classification model with 95% accuracy. Hence, piezoelectric elements integrated into a total hip stem represent a promising sensor option for the energy-autonomous detection of joint forces and physical activities.
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Past, Present, and Future of New Applications in Utilization of Eddy Currents
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Nestor O. Romero-Arismendi, Juan C. Olivares-Galvan, Jose L. Hernandez-Avila, Rafael Escarela-Perez, Victor M. Jimenez-Mondragon and Felipe Gonzalez-Montañez
Technologies 2024, 12(4), 50; https://doi.org/10.3390/technologies12040050 - 09 Apr 2024
Abstract
Eddy currents are an electromagnetic phenomenon that represent an inexhaustible source of inspiration for technological innovations in the 21st century. Throughout history, these currents have been a subject of research and technological development in multiple fields. This article delves into the fascinating world
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Eddy currents are an electromagnetic phenomenon that represent an inexhaustible source of inspiration for technological innovations in the 21st century. Throughout history, these currents have been a subject of research and technological development in multiple fields. This article delves into the fascinating world of eddy currents, revealing their physical foundations and highlighting their impact on a wide range of applications, ranging from non-destructive evaluation of materials to levitation phenomena, as well as their influence on fields as diverse as medicine, the automotive industry, and aerospace. The nature of eddy currents has stimulated the imaginations of scientists and engineers, driving the creation of revolutionary technologies that are transforming our society. As we progress through this article, we will cover the main aspects of eddy currents, their practical applications, and challenges for future works.
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(This article belongs to the Collection Electrical Technologies)
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Numerical Study of the Influence of the Structural Parameters on the Stress Dissipation of 3D Orthogonal Woven Composites under Low-Velocity Impact
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Wang Xu, Mohammed Zikry and Abdel-Fattah M. Seyam
Technologies 2024, 12(4), 49; https://doi.org/10.3390/technologies12040049 - 05 Apr 2024
Abstract
This study investigates the effects of the number of layers, x-yarn (weft) density, and z-yarn (binder) path on the mechanical behavior of E-glass 3D orthogonal woven (3DOW) composites during low-velocity impacts. Meso-level finite element (FE) models were developed and validated for 3DOW composites
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This study investigates the effects of the number of layers, x-yarn (weft) density, and z-yarn (binder) path on the mechanical behavior of E-glass 3D orthogonal woven (3DOW) composites during low-velocity impacts. Meso-level finite element (FE) models were developed and validated for 3DOW composites with different yarn densities and z-yarn paths, providing analyses of stress distribution within reinforcement fibers and matrix, energy absorption, and failure time. Our findings revealed that lower x-yarn densities led to accumulations of stress concentrations. Furthermore, changing the z-yarn path, such as transitioning from plain weaves to twill or basket weaves had a noticeable impact on stress distributions. The research highlights the significance of designing more resilient 3DOW composites for impact applications by choosing appropriate parameters in weaving composite designs.
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(This article belongs to the Section Innovations in Materials Processing)
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Carbon Fiber Polymer Reinforced 3D Printed Composites for Centrifugal Pump Impeller Manufacturing
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Gabriel Mansour, Vasileios Papageorgiou and Dimitrios Tzetzis
Technologies 2024, 12(4), 48; https://doi.org/10.3390/technologies12040048 - 03 Apr 2024
Abstract
Centrifugal pumps are used extensively in various everyday applications. The occurrence of corrosion phenomena during operation often leads to the failure of a pump’s operating components, such as the impeller. The present research study examines the utilization of composite materials for fabricating centrifugal
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Centrifugal pumps are used extensively in various everyday applications. The occurrence of corrosion phenomena during operation often leads to the failure of a pump’s operating components, such as the impeller. The present research study examines the utilization of composite materials for fabricating centrifugal pump components using additive manufacturing as an effort to fabricate corrosion resistant parts. To achieve the latter two nanocomposite materials, carbon fiber reinforced polyamide and carbon fiber reinforced polyphenylene sulfide were compared with two metal alloys, cast iron and brass, which are currently used in pump impeller manufacturing. The mechanical properties of the materials are extracted by performing a series of experiments, such as uniaxial tensile tests, nanoindentation and scanning electron microscope (SEM) examination of the specimen’s fracture area. Then, computational fluid dynamics (CFD) analysis is performed using various impeller designs to determine the fluid pressure exerted on the impeller’s geometry during its operation. Finally, the maximum power rating of an impeller that can be made from such composites will be determined using a static finite element model (FEM). The FEM static model is developed by integrating the data collected from the experiments with the results obtained from the CFD analysis. The current research work shows that nanocomposites can potentially be used for developing impellers with rated power of up to 9.41 kW.
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(This article belongs to the Section Manufacturing Technology)
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Impact Localization for Haptic Input Devices Using Hybrid Laminates with Sensoric Function
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René Schmidt, Alexander Graf, Ricardo Decker, Stephan Lede, Verena Kräusel, Lothar Kroll and Wolfram Hardt
Technologies 2024, 12(4), 47; https://doi.org/10.3390/technologies12040047 - 01 Apr 2024
Abstract
The required energy savings can be achieved in all automotive domains through weight savings and the merging of manufacturing processes in production. This fact is taken into account through functional integration in lightweight materials and manufacturing in a process close to large-scale production.
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The required energy savings can be achieved in all automotive domains through weight savings and the merging of manufacturing processes in production. This fact is taken into account through functional integration in lightweight materials and manufacturing in a process close to large-scale production. In previous work, separate steps of a process chain for manufacturing a center console cover utilizing a sensoric hybrid laminate have been developed and evaluated. This includes the process steps of joining, forming and inline polarization as well as connecting to an embedded system. This work continues the research process by evaluating impact localization methods to use the center console as a haptic input device. For this purpose, different deep learning methods are derived from the state of the art and analyzed for their applicability in two consecutive studies. The results show that MLPs, LSTMs, GRUs and CNNs are suitable to localize impacts on the novel laminate with high localization rates of up to 99%, and thus the usability of the developed laminate as a haptic input device has been proven.
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(This article belongs to the Section Innovations in Materials Processing)
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Enhancing Patient Care in Radiotherapy: Proof-of-Concept of a Monitoring Tool
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Guillaume Beldjoudi, Rémi Eugène, Vincent Grégoire and Ronan Tanguy
Technologies 2024, 12(4), 46; https://doi.org/10.3390/technologies12040046 - 29 Mar 2024
Abstract
Introduction: A monitoring tool, named Oncology Data Management (ODM), was developed in radiotherapy to generate structured information based on data contained in an Oncology Information System (OIS). This study presents the proof-of-concept of the ODM tool and highlights its applications to enhance patient
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Introduction: A monitoring tool, named Oncology Data Management (ODM), was developed in radiotherapy to generate structured information based on data contained in an Oncology Information System (OIS). This study presents the proof-of-concept of the ODM tool and highlights its applications to enhance patient care in radiotherapy. Material & Methods: ODM is a sophisticated SQL query which extracts specific features from the Mosaiq OIS (Elekta, UK) database into an independent structured database. Data from 2016 to 2022 was extracted to enable monitoring of treatment units and evaluation of the quality of patient care. Results: A total of 25,259 treatments were extracted. Treatment machine monitoring revealed a daily 11-treatement difference between two units. ODM showed that the unit with fewer daily treatments performed more complex treatments on diverse locations. In 2019, the implementation of ODM led to the definition of quality indicators and in organizational changes that improved the quality of care. As consequences, for palliative treatments, there was an improvement in the proportion of treatments prepared within 7 calendar days between the scanner and the first treatment session (29.1% before 2020, 40.4% in 2020 and 46.4% after 2020). The study of fractionation in breast treatments exhibited decreased prescription variability after 2019, with distinct patient age categories. Bi-fractionation once a week for larynx prescriptions of 35 × 2.0 Gy achieved an overall treatment duration of 47.0 ± 3.0 calendar days in 2022. Conclusions: ODM enables data extraction from the OIS and provides quantitative tools for improving organization of a department and the quality of patient care in radiotherapy.
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(This article belongs to the Topic Smart Healthcare: Technologies and Applications)
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An Artificial Bee Colony Algorithm for Coordinated Scheduling of Production Jobs and Flexible Maintenance in Permutation Flowshops
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Asma Ladj, Fatima Benbouzid-Si Tayeb, Alaeddine Dahamni and Mohamed Benbouzid
Technologies 2024, 12(4), 45; https://doi.org/10.3390/technologies12040045 - 25 Mar 2024
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This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed
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This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed for each machine on the shopfloor. With such a flexible nature of maintenance activities, the resulting joint schedule is more practical and representative of real-world scenarios. Our goal is to determine the best job permutation in which flexible maintenance activities are properly incorporated. To tackle the NP-hard nature of this problem, an artificial bee colony (ABC) algorithm is developed to minimize the total production time (Makespan). Experiments are conducted utilizing well-known Taillard’s benchmarks, enriched with maintenance data, to compare the proposed algorithm performance against the variable neighbourhood search (VNS) method from the literature. Computational results demonstrate the effectiveness of the proposed algorithm in terms of both solution quality and computational times.
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Blood Pressure Measurement Device Accuracy Evaluation: Statistical Considerations with an Implementation in R
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Tanvi Chandel, Victor Miranda, Andrew Lowe and Tet Chuan Lee
Technologies 2024, 12(4), 44; https://doi.org/10.3390/technologies12040044 - 25 Mar 2024
Abstract
Inaccuracies from devices for non-invasive blood pressure measurements have been well reported with clinical consequences. International standards, such as ISO 81060-2 and the seminal AAMI/ANSI SP10, define protocols and acceptance criteria for these devices. Prior to applying these standards, a sample size of
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Inaccuracies from devices for non-invasive blood pressure measurements have been well reported with clinical consequences. International standards, such as ISO 81060-2 and the seminal AAMI/ANSI SP10, define protocols and acceptance criteria for these devices. Prior to applying these standards, a sample size of N >= 85 is mandatory, that is, the number of distinct subcjects used to calculate device inaccuracies. Often, it is not possible to gather such a large sample. Many studies apply these standards with a smaller sample. The objective of the paper is to introduce a methodology that broadens the method first developed by the AAMI Sphygmomanometer Committee for accepting a blood pressure measurement device. We study changes in the acceptance region for various sample sizes using the sampling distribution for proportions and introduce a methodology for estimating the exact probability of the acceptance of a device. This enables the comparison of the accuracies of existing device development techniques even if they were studied with a smaller sample size. The study is useful in assisting BP measurement device manufacturers. To assist clinicians, we present a newly developed “bpAcc” package in R to evaluate acceptance statistics for various sample sizes.
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(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence)
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Open AccessReview
Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges
by
Khadija Meghraoui, Imane Sebari, Juergen Pilz, Kenza Ait El Kadi and Saloua Bensiali
Technologies 2024, 12(4), 43; https://doi.org/10.3390/technologies12040043 - 24 Mar 2024
Abstract
Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast
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Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field.
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(This article belongs to the Section Information and Communication Technologies)
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